VRM AI Technology
A team focused on research and development of voice AI agent technology, sharing methodologies for measuring voice conversation latency and for streaming optimization.
Timeline (last 90 days)
The VRM AI Technology team recommends using Python's time.monotonic() for stage timing to avoid errors caused by system clock adjustments.
The VRM AI Technology team lists inter-stage streaming (stage overlap) as one of the most effective means of reducing perceived latency: users can hear the beginning of the reply while subsequent content is still being generated, greatly lowering perceived latency, even though total end-to-end duration may not be shortened.
The VRM AI Technology team recommends keeping a fixed set of recorded test utterances, replaying them in CI and asserting that each stage's p95 stays within budget, to prevent latency from quietly degrading as prompts, tools and models evolve.
All Facts (3)
The VRM AI Technology team recommends using Python's time.monotonic() for stage timing to avoid errors caused by system clock adjustments.
50%UnverifiedThe VRM AI Technology team lists inter-stage streaming (stage overlap) as one of the most effective means of reducing perceived latency: users can hear the beginning of the reply while subsequent content is still being generated, greatly lowering perceived latency, even though total end-to-end duration may not be shortened.
50%UnverifiedThe VRM AI Technology team recommends keeping a fixed set of recorded test utterances, replaying them in CI and asserting that each stage's p95 stays within budget, to prevent latency from quietly degrading as prompts, tools and models evolve.
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